Method of motion correction in optical coherence tomography imaging

ABSTRACT

An image data set acquired by an optical coherence tomography (OCT) system is corrected for effects due to motion of the sample. A first set of A-scans is acquired within a time short enough to avoid any significant motion of the sample. A second more extensive set of A-scans is acquired over an overlapping region on the sample. Significant sample motion may occur during acquisition of the second set. A-scans from the first set are matched with A-scans from the second set, based on similarity between the longitudinal optical scattering profiles they contain. Such matched pairs of A-scans are likely to correspond to the same region in the sample. Comparison of the OCT scanner coordinates that produced each A-scan in a matching pair, in conjunction with any shift in the longitudinal scattering profiles between the pair of A-scans, reveals the displacement of the sample between acquisition of the first and second A-scans in the pair. Estimates of the sample displacement are used to correct the transverse and longitudinal coordinates of the A-scans in the second set, to form a motion-corrected OCT data set.

PRIORITY

This application is a continuation of U.S. patent application Ser. No.14/242,683, filed Apr. 1, 2014, which in turn is a continuation of U.S.patent application Ser. No. 13/357,097 (now U.S. Pat. No. 8,711,366),filed Jan. 24, 2012, which in turn is a continuation of U.S. patentapplication Ser. No. 12/794,926 (now U.S. Pat. No. 8,115,935), filedJun. 7, 2010, which in turn is a continuation of U.S. patent applicationSer. No. 12/075,477 (now U.S. Pat. No. 7,755,769), filed Mar. 11, 2008,which in turn is a continuation of U.S. patent application Ser. No.11/331,567, filed Jan. 13, 2006 (now U.S. Pat. No. 7,365,856), which inturn claims the benefit of the filing date under 35 U.S.C. §119(e) ofU.S. Provisional Patent Application Ser. No. 60/645,637, filed Jan. 21,2005, each of which are hereby incorporated herein by reference in theirentirety.

TECHNICAL FIELD OF THE INVENTION

The present invention relates to data acquisition methods for imaging byoptical coherence tomography (OCT). In particular, the invention is amethod for determining patient motion occurring during the acquisitionof large sets of data by OCT. The method described herein acquires asparse set of OCT data in a sufficiently short time that patient motionduring the acquisition is not objectionable. The sparse set of OCT dataacts as a set of guideposts for determination of the locations on thesample, of the measurements comprising the full data set.

BACKGROUND OF THE INVENTION

Optical Coherence Tomography (OCT) is a technique for performinghigh-resolution cross-sectional imaging that can provide images oftissue structure on the micron scale in situ and in real time [Huang etal. (1991)]. OCT is a method of interferometry that determines thescattering profile of a sample along the OCT beam. Each scatteringprofile is called an axial scan, or A-scan. Cross-sectional images, andby extension 3D volumes, are built up from many A-scans, with the OCTbeam moved to a set of transverse locations on the sample. Motion of thesample with respect to the OCT scanner will cause the actual locationsmeasured on the sample to be arranged differently than the scan patternin scanner coordinates, unless the motion is detected and the OCT beamplacement corrected to track the motion.

In recent years, frequency domain OCT techniques have been applied toliving samples [Nassif et al. (2004)]. The frequency domain techniqueshave significant advantages in speed and signal-to-noise ratio ascompared to time domain OCT [Leitgeb, R. A., et al., (2003); de Boer, J.F. et al., (2003); Choma, M. A., et al. (2003)]. The greater speed ofmodern OCT systems allows the acquisition of larger data sets, including3D volume images of human tissue.

In the case of ophthalmology, a typical patient can comfortably hold hiseye open for a few seconds. OCT systems can advantageously use these fewseconds to collect extensive images. During such an acquisition, motionof the patients head and natural shifts in the patient's fixation willdistort the image. Tracking the motion of the eye to correct theplacement of the OCT beam has proven useful [U.S. Pat. No. 6,736,508;Hammer, D. X., et al. (2005)]. There is also motion along the OCT beam,which is not detectable by the common designs of eye trackers, but whichdoes distort the OCT image.

There is therefore a need for a method to correct the placement of OCTimage data acquired on a moving sample. The correction could be appliedto the mechanism scanning the OCT beam, to approximately follow themotion of the sample. Alternatively, the correction could be appliedwhen images are built from the A-scans acquired in the presence ofsample motion. The need is for a method to determine the motion, inthree dimensions, of the sample during the acquisition of the A-scans. Amethod that does not require an additional optical system for eyetracking would have the advantages of simplicity and lower cost.

SUMMARY OF THE INVENTION

The present invention acquires, in addition to the set of A-scanscomprising the desired image, a widely-spaced set of guidepost A-scansthat can be recorded quickly enough to avoid objectionable motion of thesample. This method compares some of the A-scans comprising the imagewith some of the guidepost A-scans. When comparison shows that theoptical scattering profile of an image A-scan and guidepost A-scanclosely match, the location of the image A-scan on the moving sample isassumed to be the same as that of the matching guidepost A-scan.

If one were to assume no sample motion, one would expect the matches tobe found during the course of the imaging scan pattern, whenever the OCTbeam probes the same location, with respect to the scanner, as it didwhen collecting one of the guideposts (i.e. the scanner coordinates ofthe OCT system would be the same). Motion of the sample will cause thematches to occur for A-scans recorded at somewhat different scannercoordinates than would have been expected under the assumption of nosample motion. Each time a match is found, comparison of the actualscanner coordinates and the expected scanner coordinates (under theassumption of no sample motion), reveals the transverse displacement ofthe sample between acquisition of the guidepost scans and theacquisition of the matching A-scan in the image set. Comparison of thecontents of the matching pair of A-scans reveals any longitudinaldisplacement of the sample, which would appear as a longitudinal shiftin the image data between the matching pair of A-scans.

The comparison between A-scans need only be done between pairs ofA-scans that are likely to match, such as those pairs which would havemeasured nearly the same location on the sample in the absence of samplemotion. Depending on the comparison method, many pairs may match to somedegree, so the method chooses the best-matching pair.

During the course of the imaging scan pattern, each match found providesupdated information on the displacement of the sample. One can estimatethe position of the sample between such matches by fitting a smoothcurve through the points determined by the matches. Given the resultingcurve of sample position versus time, the image data can be shifted toits correct locations, to form a 3D image free of motion artifact.

Other methods of determining eye motion use a landmark, such as theoptic disk. The landmark is identified first, and its location ismonitored as the detailed OCT scan proceeds. The landmark can be trackedon a separate imaging system, or the OCT beam can scan the landmarkoccasionally, briefly interrupting the larger data acquisition. However,good landmarks are not always found in diseased tissue. The methoddisclosed here takes advantage of the fact that the details in thestructure of any tissue can serve the same need as a landmark.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates schematically one type of OCT system.

FIG. 2 is an example of an OCT image of human eye tissue, showing thedetail that exists within the image.

FIG. 3 shows three views of a 3D OCT data set collected from a humaneye, in which motion artifact is visible.

FIG. 4 shows three views of a 3D OCT data set collected from a humaneye, in which motion artifact has been largely corrected by the methodsdisclosed herein.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 is a schematic illustration of one design of OCT system. Thelight source 101 provides light having a short-coherence length to thefiber-based interferometer. Directional coupler 102 serves to split thelight from source 101 between a reference arm 103 and a sample arm 104.Lens 111 and mirror 112 serve to return reference light back to coupler102. A scanning system including lenses 121 a, 121 b, and 121 c, andscanning mirrors 122, directs light successively along paths such as 123a, 123 b, 123 c onto successive locations on sample 125. Some lightscattered from sample 125 returns closely enough along the illuminationpath to re-enter the fiber interferometer, is combined with referencelight in coupler 102. The interfered sample and reference light aredetected in detector 130.

The scanning mirror 122 is controlled by a system processor andgenerates scan coordinates which correspond to certain transversepositions on the sample. A sample, such as the human eye, will move withrespect to the OCT system. Once the sample moves the scanner coordinatesassociated with a particular transverse position on the sample willchange in an unknown manner. The subject application describes a methodfor determining the extent of this displacement and correcting for thatdisplacement.

As noted above, the sample 125 may move with respect to the measurementsystem causing a time-dependent difference between scanner coordinatesand sample coordinates. In some OCT systems, such as handheld scanners,motion of the scanning optics can contribute to the relative motionbetween scanner coordinates and sample coordinates.

FIG. 2 shows an example OCT tomogram from human tissue. Each column inthis image is one A-scan; successive A-scans at neighboring locations intissue are placed side-by-side to form the image. The grey scale isinverted, with highly scattering regions shown dark, to better reproducethe structure within the image that this method uses to recognizeregions of tissue.

Referring to FIG. 3, a three dimensional data set acquired from a humaneye is shown in three views. En-face projection 300 is the integratedoptical scattering, integrated along depth z to produce a view similarto what one would see in an opthalmoscope. Sections 301 and 302 showsections of the volume in which one axis z is the direction ofpropagation of the OCT beam. Section indicators 301 a and 302 a show thelocations of sections 301 and 302, respectively, in projection 300.Sections such as 301 and 302 are called tomograms. The columnscomprising tomogram 301, and the rows comprising tomogram 302, areindividual A-scans. Each pixel in the projection 300 is the integratedintensity from one A-scan. The dimensions of the tissue within the imageare approximately 3 mm along each of x and y, 1.5 mm along z.

The acquisition of A-scans for this example proceeded along thehorizontal rows of projection 300. Tomogram 301 is one of these rows, sothe A-scans comprising this section were acquired sequentially. Tomogram302 consists of A-scans chosen from successive rows, so one sees insection 302 the motion of the sample during the duration of theacquisition of the full 3D volume.

In addition to the raster of A-scans, we acquire a set of guidepostA-scans, which is represented in FIG. 3 by the lower-resolution en-faceimage 350 composed of the integrated optical scattering in the set ofguidepost A-scans en-face image. The preferred embodiment usesapproximately a 32-by-32 grid of guidepost A-scans, which can beacquired within 100 milliseconds by modern OCT techniques. For purposesof illustration, the example of FIG. 3 uses a guidepost set consistingof 30 rows of 128 A-scans; this larger set produces a more recognizableen-face image 350, while still requiring less than 200 milliseconds foracquisition by modern OCT techniques.

The set of guidepost A-scans is preferably acquired quickly enough thatthe sample is substantially stationary, meaning that there is noobjectionable motion of the sample. For example, when imaging the retinaof the human eye, the transverse optical resolution is typically nobetter than 5 microns. The fast motions of the eye called tremor causeonly a few microns apparent motion of the retina, apparent motions beingthe motion as seen through the optics of the human eye. Motions due totremor are typically not resolvable, so they are not consideredobjectionable. The jerk-like eye rotations called saccades can cause 100microns apparent motion, so they are objectionable. Saccades occurirregularly, approximately once per second. Thus when applied to OCT ofthe human eye, the set of guidepost A-scans must be acquired withinsignificantly less than one second in order to avoid objectionablemotion. Acquisition of the guidepost A-scans within 200 milliseconds ispreferred in this application.

For comparison purposes, the full raster scan of image information shownin FIG. 3 corresponds to 256 rows of 256 A-scans. Using currenttechnology, this raster scan can take on the order of 1 to 2 seconds toperform during which time the eye will move.

There are alternatives to a raster scan for collecting the OCT imaginginformation. One can use a set of radial scans, scanning transverselyacross a set of lines that extend outward from a center point; radialscans in this pattern have the characteristic of higher density samplingnear the crossing point. Another alternative pattern is a set ofconcentric circles. Because the currently described method takesadvantage of revisiting the portions of the sample seen duringcollection of a set of guideposts, the method provides the greatestadvantage when the scan pattern for the full data set covers atwo-dimensional area. Scan patterns with a two-dimensional transverseextent can be collected using a scan pattern designed to frequentlycross a set of guideposts, with the guideposts conveniently collectedusing one or a few transverse scan lines. A two-dimensional extent inthis context is an area having transverse dimensions more than ten timesthe transverse optical resolution, so that meaningful tomogram imagescan be extracted in each transverse dimension, such as tomograms 301 and302 in FIG. 3.

The guidepost A-scans are compared with A-scans in the raster for thepurpose of finding matches of the scattering profile. One effectivecomparison method is the normalized cross-correlation, defined by

X ₁₂(t)≡∫a ₁*(z)·a ₂(z−t)dz/√{square root over (∫a ₁ ² dz·∫a ₂ ² dz)}

where a(z) is a measure of the scattering intensity along the OCT beam,t is a variable indicating the relative shift along the beam directionof the two A-scans, and the * denotes complex conjugation. The A-scansa₁ and a₂ are chosen from the guidepost set and the raster,respectively.

If the A-scans were measured at locations more closely spaced than thewidth of the OCT beam, then there will likely be one A-scan in each rowof the full data set which correlates well with one guidepost A-scan. Ifthere has been eye motion in x and y between acquisition of theguideposts and acquisition of the raster scan, the pair of A-scans withthe highest cross-correlation will consist of two A-scans acquired atdifferent sets of scanner coordinates of the OCT system. The differencein scan coordinates reveals the transverse displacement of the samplebetween acquisition of the guideposts and acquisition of the matchingA-scan in the raster.

The cross-correlation is a function of the shift t. The value of t atwhich X₁₂(t) is maximized reveals the longitudinal displacement of thesample, along z, between acquisition of the guideposts and acquisitionof the matching A-scan in the raster. In this way the displacement in x,y and z of the sample is determined at several points along the rasterscan, each time a good match is found between an A-scan in the rasterand an A-scan in the set of guideposts. The location of the samplebetween such matches is estimated by fitting a smooth curve, as afunction of progress time of the raster scan, through the pointsdetermined by the matches. Given the resulting curve of sampledisplacement versus time, the data in the raster of A-scans can beshifted and interpolated to form a three-dimensional data set that isfree of motion artifact.

The method disclosed herein is most effective when used to accuratelycorrect minor movements during acquisition of the image A-scans. It canbe advantageously combined with prior art methods that are better suitedto correct sudden motions, such as saccades of the eye. One prior artmethod that is better suited to correction of saccades iscross-correlation of each tomogram in the image set of A-scans with itsimmediate neighbors; this prior art method relies on continuity ofanatomical features between tomograms. The method disclosed herein couldalso be advantageously combined with coarse tracking of sample motion;coarse tracking may be more practical to implement than trackingaccurate enough to eliminate all objectionable displacement between thedesired and actual measurement locations.

FIG. 4 shows the data of FIG. 3 after correction using the methoddescribed herein. The data contained in the image A-scans has beenshifted, and interpolated as necessary, so that each image A-scans thatmatched a guidepost A-scan now appears in a position corresponding tothe location where that guidepost A-scan was acquired. En-faceprojection 400 is built from the optical scattering in this correcteddata set, integrated along depth z. Tomograms 401 and 402 are sectionsthrough the corrected data set, related to projection 400 as indicatedby section indicators 401 a and 402 a respectively. Indication 450 showsthe location extent of the guidepost set 350 in relation to the full setof image A-scans.

The motion artifact seen in tomogram 302 is corrected in tomogram 402.Tomogram 402 represents the true shape of the imaged tissue section.Correction of the en-face projection 400 is more subtle, but can benoted by comparing it with en-face projections 300 and 350.

An alternative pattern for the set of guideposts would be to form one ormore generally diagonal sections across the volume to be covered by thefull data set. Then in the absence of motion, each row of A-scans in theraster will be likely to match one of the A-scans in each diagonalsection. However, there can be occasional erroneous matches betweenA-scans acquired at two different locations in tissue, when the tissueat these two locations happens by chance to have similar opticalscattering profiles. Such erroneous matches would cause distortion inthe output. The grid pattern of guidepost A-scans used in the examplesof FIG. 3 and FIG. 4 tends to provide clusters of matches to the imageA-scans; that is, some rows in the raster contain several neighboringA-scans that have matching guidepost A-scans. Having matches occur inclusters, coupled with the knowledge that matches within a clustershould show nearly the same sample displacement due to their proximityin time, plus a step of fitting the displacements determined by thematches to a smooth curve in time, gives protection against erroneousmatches. Patterns of guideposts that cause matches to occur in clustersare therefore preferred.

A number of practical complications can be accommodated in this method.There are some choices for the function a(z) used to represent eachA-scan. In many methods of OCT, including the modern Fourier-domainmethods, the information on the amplitude and phase of the scatteredlight is recorded; a(z) could be chosen to be this complex-valuedfunction. However, between acquisition of the guidepost scans andacquisition of the corresponding A-scans in the raster, the relativephases of the light scattered from different depths may have changed.This will happen if scattering centers move relative to one another byeven a fraction of a wavelength of the probe light. Such motion islikely in living tissue, and such changes in a(z) would prevent one fromfinding a good match. For this reason, a(z) is best taken to be thereal-valued intensity of light scattered from depth z, so that thecomparison ignores the phase information.

OCT typically measures the scattering with an axial resolution of a fewmicrons. Motion of some tissues, such as blood vessels, can change thescattering profile over this length scale. Also, if the spacing ofA-scans in the raster is not much less than the OCT beam diameter, thenno A-scan in the raster will exactly match any guidepost A-scan. One canapply smoothing of the scattered intensity, as measured by OCT, and usethe values in the smoothed data set to form the function a(z). Thepreferred amount of smoothing retains as much detail in the A-scans ascan be expected to be found upon re-measuring the same tissue, given themotion within the tissue and the coarseness of the raster scan.

Failures to find well-correlated A-scans will still occur. Eye motionduring acquisition of a row in the raster could cause a failure, if theeye motion causes the raster to skip over the region covered by one ofthe diagonal traces in the guidepost set. Intermittent failures in thescan, due to dust on optics or floaters in the eye, could make a fewa-scans un-useable. Eye motion greater than the extent of the regionscanned will also cause registration failures. If the comparison methodis cross-correlation, failed matches are recognized by their lowcross-correlation coefficient, relative to the coefficient found innon-moving similar tissue. The effect of failed matches is mitigated bythe step fitting of a smooth curve through the displacements determinedby successful matches. Failed matches can be omitted from the fit, orthe cross-correlation coefficient can be used as a confidence value inweighted fitting.

We consider now efficient application of the preferred embodiment toFourier-domain methods of OCT, which record interference spectra at eachtransverse location on the sample. It is desirable to have a method offinding the proper position of an A-scan quickly directly from thespectrum, without taking the Fourier transform required to reconstructthe A-scan. Calculating the displacement of the sample quickly isdesired to allow correction of the raster scan position during theraster scan, so that areas are not skipped due to motion of the patientagainst the direction of the scan.

The cross-correlation X₁₂ can be efficiently calculated from the spectrathat are directly provided in Fourier-domain methods of OCT. Thecross-correlation between two A-scans is the Fourier transform of theproduct of the two corresponding fringe spectra (the portion of thespectra due to interference, after subtracting the intensity of thereference beam). Each A-scan is related to a fringe spectrum through

a(z)=∫e ^(i2qz) S(q)dq,

where S(q) is the fringe spectrum as a function of the optical frequencyq, with q in units of radians of optical phase per unit length. Thecross-correlation is related to the fringe spectra by

X ₁₂(t)≡∫e ^(i2q·t) S ₁*(q)·S ₂(q)dq/√{square root over (∫S ₁ ² dz·∫S ₂² dz)}.

The cross-correlation between a pair of A-scans, if the A-scans arerecorded at the same transverse location on the patient to within thebeam diameter, has a peak at a position corresponding to the motionalong the z-axis between acquisition of the two A-scans. The size ofthese peaks will be greatest for the pair of A-scans that best overlapin the transverse directions. Thus, the integrated squared magnitude ofthe cross-correlation indicates quantitatively the goodness of matchbetween a pair of A-scans, and this integrated magnitude is the same asthe integrated squared magnitude of the point-by-point product of thecorresponding spectra, by Parseval's theorem. The integrated squaredmagnitude of the cross-correlation is efficiently calculated from

C ₁₂ =∫|X ₁₂(z)|² dz≡∫|S ₁(q)S ₂*(q)|² dq/√{square root over (∫S ₁ ²dq·∫S ₂ ² dq)}.

The quantity C₁₂, computed for each pair of A-scans that is a candidatematch, is used to identify matching pairs and thus the sampledisplacement in x and y. Then the value of the shift t at which X₁₂ hasits peak value is used to determine the sample displacement in z.

One further complication is that Fourier-domain OCT techniques thatrecord a single interference spectrum for each A-scan, with asingle-phase of the reference beam, produce a double image. Each A-scana(z) consists of the reflectivity profile along z, plus the mirror imageof that profile. The cross-correlation is complicated by this mirrorimage, having two peaks instead of one: a peak at the positioncorresponding to the z-motion of the patient between acquisitions andanother peak at the position corresponding to the negative of thez-motion of the patient.

One can avoid this problem by eliminating the part of the A-scancorresponding to positions z<0. Working directly on the spectra, one caneliminate this part of the corresponding A-scan by convoluting with akernel that has a Fourier transform that is 1 for most of the z>0 axis,and 0 for most of the z<0 axis. An example of such a kernel is (−i/4,1/2, +i/4). Convolution with a short kernel such as this one is moreefficient than computing the Fourier transform; the convolutionoperation could be seen as an efficient approximation to the Hilberttransform. The resulting fringe spectra are complex valued and have aFourier transform that is largely attenuated along the range z<0. Afterperforming this convolution, the cross-correlation can be computed asabove.

Thus an efficient method to obtain the integrated squared magnitude ofthe cross-correlation between two A-scans, using only the two spectracomprising the SD-OCT measurements of two A-scans, is as follows: 1)Isolate the fringe part of the spectra by subtracting the referencebeam. 2) Perform an approximate Hilbert transform on each spectrum, forexample by convolution with the truncated kernel described in theparagraph above. 3) Form the sums of the pointwise products of pairs oftransformed spectra, to obtain cross-correlations C₁₂.

An extension of this fast cross-correlation technique provides analternative method to estimate the sample displacement along z. Thecross-correlation has a peak at a position corresponding to the motionalong z, and the magnitude of the cross-correlation is symmetric aboutthat peak. Therefore an estimate of the motion can be extracted bycomputing the average value of z, weighted by the squared magnitude ofthe cross-correlation:

$\begin{matrix}{{\Delta \; z} = {\frac{1}{C_{12}}{\int{{z \cdot {{X_{12}(z)}}^{2}}{z}}}}} \\{= {\frac{1}{C_{12}}{\int{{S_{1}^{*}(q)}{S_{2}(q)}\frac{1}{2i}{\frac{\;}{q}\left\lbrack {{S_{1}(q)}{S_{2}^{*}(q)}} \right\rbrack}{q}\text{/}\sqrt{\int{S_{1}^{2}{{q} \cdot {\int{S_{2}^{2}{q}}}}}}}}}}\end{matrix}$

The longitudinal displacement is given by Δz calculated by the formulaabove, instead of by searching the function X₁₂(t) for its peak value.

This method allows several variations and extensions. The scan patterncollecting the main OCT image need not be a raster scan, but can be anyset of A-scans that build a useful OCT data set. This method is usefulso long as a set of guidepost A-scans can be acquired in locations thatare likely to be included in the full OCT data set, given expectedmotion of the sample. The set of A-scans serving as guideposts can beacquired before or after the main OCT image. The comparison functionneed not be a cross-correlation, but can by any measure of thelikelihood that two A-scans have measured the same location on thesample. Some alternate methods to compare candidate pairs of A-scansare: their mean-square difference, their Kullback Leibler distance, orthe mutual information metric between the candidate pair. The comparisonmethod can be applied to OCT optical scattering data in which the phaseof the scattering is preserved, to OCT optical scattering data reducedto an intensity, or to logarithmically scaled intensities. Methods toreduce random noise in OCT data, such as thresholding and medianfiltering, have been described in the art and can be applied to the OCTdata before comparison.

Although various embodiments that incorporate the teachings of thepresent invention have been shown and described in detail herein, thoseskilled in the art can readily devise many other varied embodiments thatstill incorporate these teachings.

The following references are hereby incorporated herein by reference.

US Patent Documents

Application US20050140984 Hitzenberger. “Efficient optical coherencetomography (OCT) system and method for rapid imaging in threedimensions”

U.S. Pat. No. 6,736,508 Xie, Kohnle, and Wei. “Tracking assisted opticalprocedure”

U.S. Pat. No. 4,937,526 Ehman and Felmlee “Adaptive Method for ReducingMotion and Flow Artifacts in NMR Images”

Co-pending U.S. patent application Ser. No. 10/750,341, Hitzenberger, P.“Efficient optical coherence tomography (OCT) system and method forrapid imaging in three dimensions”

US20030199769, Podoleanu, A. et al. (2002) “Apparatus for highresolution imaging of moving organs” (WO03086181)

OTHER PUBLICATIONS

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1. (canceled)
 2. A method for collecting optical coherence tomography(OCT) image data of a region of a sample, said method comprising:acquiring a first set of A-scans over the region of the sample byscanning light over a plurality of locations in the region, said firstset being acquired quickly enough that the sample is substantiallystationary during the acquisition; acquiring a second set of A-scansover the region of the sample by scanning light over a plurality oflocations in the region; while acquiring the second set of A-scans,comparing A-scans in the first and second sets to identify A-scans takenat the same locations within the region; using the identified A-scans tocalculate a displacement of the sample; and adjusting the scanninglocation for subsequent A-scans based on the calculated displacement. 3.A method as recited in claim 2, wherein the sample is an eye.
 4. Amethod as recited in claim 2, wherein the A-scans in the first andsecond sets are taken at different spacings over the region.
 5. A methodas recited in claim 2, wherein the comparison involvescross-correlations.
 6. A method as recited in claim 2, wherein thescanning is performed in a raster pattern.
 7. A method as recited inclaim 2, wherein the scanning is performed in a radial or circularpattern.
 8. A method as recited in claim 2, wherein the displacement isa transverse displacement.
 9. A method as recited in claim 2, whereinthe displacement is a longitudinal displacement
 10. A method as recitedin claim 2, wherein the first set of A-scans is collected in less than200 ms.
 11. A method as recited in claim 2, wherein the first set ofA-scans is collected in less than 100 ms.
 12. An optical coherencetomography (OCT) device for imaging a region of a sample comprising: alight source for generating a beam of radiation; a beam divider fordirecting a first portion of the light into a reference arm and a secondportion of the light into a sample arm; optics for scanning the beam inthe sample arm over a plurality of locations in the region of thesample; a detector for measuring light radiation returning from thesample and reference arms and generating output signals in responsethereto; and a processor for converting the output signals into threedimensional image information; said processor for controlling thescanning optics in a manner to acquire a first set of A-scans at aplurality of locations in the region, said first set being acquiredquickly enough that the sample is substantially stationary during theacquisition; said processor for controlling the scanning optics in amanner to acquire a second set of A-scans at a plurality of locations inthe region; while acquiring the second set of A-scans, comparing A-scansin the first and second sets to identify A-scans taken at the samelocations within the region; said processor calculating a displacementof the sample based on the comparison; said processor adjusting thecoordinates of the scanning optics for subsequent A-scans based on thecalculated displacement.
 13. A device as recited in claim 12 wherein thesample is an eye.
 14. A device as recited in claim 12, wherein theA-scans in the first and second sets are taken at different spacingsover the region.
 15. A device as recited in claim 12, wherein thecomparison involves cross-correlations.
 16. A device as recited in claim12, wherein the scanning is performed in a raster pattern.
 17. A deviceas recited in claim 12, wherein the scanning is performed in a radial orcircular pattern.
 18. A device as recited in claim 12, wherein thedisplacement is a transverse displacement.
 19. A device as recited inclaim 12, wherein the displacement is a longitudinal displacement
 20. Adevice as recited in claim 12, wherein the first set of A-scans iscollected in less than 200 ms.
 21. A device as recited in claim 12,wherein the first set of A-scans is collected in less than 100 ms.